ArticleJournal of the American Academy of Child and Adolescent Psychiatry2021
Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents.
Article in Journal of the American Academy of Child and Adolescent Psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for longitudinal trajectories of depression and anxiety: a systematic review.Journal of affective disorders · 2026Pooled it
- Psychological and contextual risk factors for first-onset depression among adolescents and young people around the globe: A systematic review and meta-analysis.Early intervention in psychiatry · 2023Pooled it
- Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges.Molecular psychiatry · 2022Pooled it
- Predicting 7-year-olds mental health in the perinatal period: Development and internal validation of a multivariable model using the prospective ALSPAC cohort.JCPP advances · 2026Article
- Illuminating the complex interplay of risk factors for depression symptoms within a large-scale US longitudinal cohort.Social psychiatry and psychiatric epidemiology · 2026Article
- Combining self-regulation and poverty reduction to prevent depression and anxiety among adolescents experiencing multi-dimensional poverty in Colombia, Nepal and South Africa: study protocol of a pilot 4-arm cluster randomised controlled trial.Pilot and feasibility studies · 2026Article
- Combining neurobiological markers and a sociodemographic risk score to predict adolescent depression - An IDEA RiSCo prospective cohort study.Molecular psychiatry · 2026Article
- 'It is just a prediction; it's, like, not fact': youth attitudes towards risk prediction tools and personalised preventive interventions for depression and anxiety.BMJ mental health · 2026Article
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Longitudinal Prediction of Adolescent Depression from Environmental and Polygenic Risk Scores.medRxiv : the preprint server for health sciences · 2025Article
- Assessing a prediction model for depression risk using an early adolescent sample with self-reported depression.JCPP advances · 2025Article
- Predicting the incidence of depression in adolescence using a sociodemographic risk score: prospective follow-up of the IDEA-RiSCo study.BMJ mental health · 2025Article
- Depression in left-behind adolescents from single-parent families: a nomogram based on multidimensional risk factors.Child and adolescent psychiatry and mental health · 2025Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
- A prognostic model for predicting functional impairment in youth mental health services.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- Inflammation and immune system pathways as biological signatures of adolescent depression-the IDEA-RiSCo study.Translational psychiatry · 2024Article
- Article
- Associations between perceived parenting, brain activity and connectivity, and depression symptoms in Brazilian adolescents.International journal of adolescence and youth · 2024Article
- No prediction without prevention: A global qualitative study of attitudes toward using a prediction tool for risk of developing depression during adolescence.Global mental health (Cambridge, England) · 2024Article
- Strengthening self-regulation and reducing poverty to prevent adolescent depression and anxiety: Rationale, approach and methods of the ALIVE interdisciplinary research collaboration in Colombia, Nepal and South Africa.Epidemiology and psychiatric sciences · 2023Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
objectivePrediction models have become frequent in the medical literature, but most published studies are conducted in a single setting. Heterogeneity between development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18 and to evaluate its generalizability in 2 samples from diverse socioeconomic and cultural settings.
methodData from the 1993 Pelotas Birth Cohort were used to develop the prediction model, and its generalizability was evaluated in 2 representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study.
resultsAt age 15, 2,192 adolescents with no evidence of current or previous depression were included (44.6% male). The apparent C-statistic of the models derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among samples improved the model's calibration in both samples.
conclusionAn adolescent depression risk score comprising easily obtainable predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research.
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